Standardized uptake value (SUV_max) in ¹⁸F-FDG PET/CT is correlated with the total number of main oncogenic anomalies in cancer patients

ABSTRACT

Cancer diagnosis and therapy is quickly moving from the traditional histology-based approaches to genomic stratification, providing a huge opportunity for radiogenomics, associating imaging features with genomic data. Genome sequencing is time consuming, expensive and invasive whereas ¹⁸F-FDG PET/CT is readily available, fast and noninvasive. The aim of this study was to determine the relationship between the maximum standardized uptake value (SUV_max) and the frequency of 11 common oncogenic anomalies determined by specific common genomic alterations in tissue biopsies from patients with cancer. We retrospectively studied 102 consecutive untreated patients with gastrointestinal, lung, and breast cancer who underwent ¹⁸F-FDG PET/CT imaging, shortly prior to molecular testing by a biopsy for genomic profiling that consisted of 11 common DNA alterations: (1) TP53, (2) DNA repair, (3) EGFR, (4) PI3K/AKT/MTOR (PAM) pathway including PTEN, PIK3CA, AKT, TSC, CCNB1, MTOR, FBXW2, and NF2, (5) MEK, (6) CYCLIN including CCND,CDK, CDKN, and RB, (7) WNT, (8) ALK, (9) MYC, (10) MET, and (11) FGF/FGFR. Higher SUV_max was associated with the presence of TP53 and PAM genomic anomalies (p < .05), but not the other 9 gene groups (p > .05). More importantly, SUV_max was positively correlated with total number of oncogenic anomalies (r = 0.27, p = .005). We propose higher SUV_max as an indicator for total number of common oncogenic anomalies. This finding is a step forward in noninvasive stratification of cancer patients, in terms of the overall load of oncogenic anomalies, based on their SUV_max.

KEYWORDS:

• Radiogenomics
• oncogenic anomalies
• genomic alterations
• ¹⁸f-Fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET)
• maximum standardized uptake value (SUV_max)
• cancer
• imaging

Introduction

The relatively new field of cancer genome sequencing is changing the landscape of oncology.¹ Radiogenomics plays an important role by finding relationship and creating link between imaging profiles and genomics.² Radiogenomics has been used mainly in computer tomography (CT) and magnetic resonance imaging (MRI) to draw a link between imaging features and tumor genotypes.³ However, radiogenomics in the field of nuclear medicine is not explored to the same extent, although ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) is regularly used for cancer diagnosis and treatment follow up.^4–6 Standardized uptake value (SUV_max) is routinely used as a quantitative indicator of ¹⁸F-FDG uptake, thus glucose metabolism rate (GMR). SUV_max has been shown to correlate with P53 alterations and its relationship with a variety of genomic alteration has been studied previously; however, it remains mostly nonspecific at the molecular and genetic level.^7–9

There are several common genomic anomalies that play role in tumorigenesis.¹ Determining the presence of each genomic alteration is necessary for targeted therapy. The total number of common oncogenic anomalies is also crucial information, as an indicator of the overall load of oncogenic anomalies.^10,11

Increased SUV_max is a hallmark of cancer. Although commonly explained as a result of increased proliferation rate, the genetic underpinning of increased SUV_max in cancer is still not clearly understood.^12,13 Oncogenic anomalies are proposed to be associated with metabolic reprogramming in cancer cells, causing increased GMR; however, such an association has not yet been established, clinically.¹⁴ In our prior study, we showed a potential relationship between SUV_max and the number of genomic anomalies of the tumor; however, we analyzed only a small subset of patients (N = 3) at the two extremes of SUV_max.¹³ Herein, we sought to study if higher total number of these genomic anomalies is associated with higher SUV_max, in the biopsied lesions. Genome sequencing is time consuming, expensive and invasive whereas ¹⁸F-FDG PET/CT is readily available, fast and noninvasive. Our hypothesis is that the total number of oncogenic anomalies is related to SUV_max, in the biopsied lesion.

Results

Patients

We studied 102 untreated patients including 42 (41.2%) breast, 26 (25.5%) gastrointestinal, and 34 (33.3%) lung cancer patients. Due to the retrospective nature of this study, the patients were in different stages of their cancers and the lesion biopsied included primary or metastatic lesions.

Associations between individual genes/gene families and SUV_max

Forty seven of the 102 patients (46%) had anomalies in TP53 and a significantly higher mean SUV_max of 9.0 compare to patients with unaltered TP53 which had a mean SUV_max of 6.3 (p = .002). Forty of the 102 patients (39%) had abnormalities in PAM pathway genes (PTEN, PIK3CA, AKT, TSC, CCNB1, MTOR, FBXW2, and NF2). The mean SUV_max for these 40 patients was 8.7, significantly higher than 6.8 in patients without abnormal PAM pathway genes (p = .042).

Four of 102 patients (4%) had DNA repair gene (BRCA, BRIP, ATM, MMR, MSH, MLH) anomalies. The mean SUV_max of these 4 patients was 9.7, not significantly different from 7.4 in those without DNA repair gene anomalies (p = .28). Twenty eight of 102 patients (27%) had EGFR gene anomalies. The mean SUV_max of these 28 patients was 7.6, not significantly different from 7.5 in those without EFGR gene anomalies (p = .98). Eighteen of the 102 patients (18%) had abnormalities in MEK genes (RAS, RAF, MAPK, and CNAS). The mean SUV_max for these 18 patients was 8.9, not significantly higher than 7.2 in patients without abnormal MEK pathway genes (p = .16). Thirty nine of the 102 patients (38%) had abnormalities in CYCLIN pathway genes (CCND, CDK, CDKN, RB) and a mean SUV_max of 7.8 compared to 7.4 in the patient without CYCLIN abnormality. These means were not significantly different (p = .68). Seventeen of the 102 patients (17%) had abnormalities in WNT genes (APC, CTNNB, NOTCH) and a mean SUV_max of 8.2 compared to 7.4 in the patient without WNT abnormality. These means were not significantly different (p = .52). Three of the 102 patients (3%) had abnormalities in ALK gene and a mean SUV_max of 11.9 compared to 7.4 in the patient without ALK abnormality. These means were not significantly different (p = .09). Fourteen of the 102 patients (14%) had MYC gene anomalies and a corresponding mean SUV_max of 8.7, not significantly different compared to the 88 patients without MYC gene anomalies with a mean SUV_max of 7.4 (p = .31). Two of the 102 patients (2%) had MET gene anomalies and a corresponding mean SUV_max of 6, not significantly different compared to the 100 patients without MET gene anomalies with a mean SUV_max of 7.6 (p = .63). Twenty one of the 102 patients (21%) had FGF/FGFR gene anomalies and a mean SUV_max of 6.8 as compared to the 81 patient without anomalies and a mean SUV_max of 7.7. This was not significant (p = .40).

Total number of main oncogenic anomalies

Total number of oncogenic anomalies from the aforementioned 11 genetic groups was calculated for each patient from summation of the individual genomic anomalies. This number actually ranged from 0 to 6, as no patient had more than 6 oncogenic anomalies. In fact, 12 patients (12%) had no genetic anomalies, 19 patients (19%) had 1 genetic alteration, 30 patients (29%) had 2 total anomalies, 17 patients (17%) had 3 total anomalies, 18 patients(18%) had total of 4 anomalies, 5 patients (5%) had total of 5 anomalies, and 1 patient (1%) had 6 oncogenic anomalies (Figure 1). No patient had more than 6 oncogenic anomalies. The SUV_max was positively correlated to the total of oncogenic anomalies (r = 0.27, p = .005).

Figure 1. Relationship between SUV_max and the total number of oncogenic alterations with Pearson correlation coefficient r = 0.27 (p = .005). Left panel shows the scatter plot of all patients (n = 102) with circles represent individual datapoints. Right panel shows the box plot for all patients excluding the group with 6 oncogenic alterations because there was only one patient in that group. The central thick black line indicates the median, and the bottom and top of the rectangle are the 25^th (Q1) and 75^th (Q3) percentiles. The circles represent outlier SUV_max, defined as either larger than Q3 + 1.5 × IQR or smaller than Q1 – 1.5 × IQR, where IQR = Q3 – Q1 is the interquartile range. The horizontal “whiskers” represent the largest and smallest non-outlier observations in the data set

Discussion

Radiogenomics, defined as associating imaging features with genomic data, is gaining attention as the precision medicine is rapidly evolving.^{15,16 18}F-FDG PET/CT is standard of care for cancer staging/restaging, to guide therapeutic decisions and monitor therapeutic response. In our prior study, we used a simple method where we ranked the patients based on SUV_max. Then we selected three patients with the highest SUV_max and three patients with the lowest SUV_max, and speculated that the patients with the highest SUV_max group had more genomic anomalies.¹³ In the current study, we performed a rigorous statistical analysis of a more homogenous subset of the previous study population, containing only patients with breast, lung and gastrointestinal tumors. In addition, the analysis of the relationship between SUV_max and total number of genetic anomalies was performed on all 102 patients. The aim of this study was to determine the relationship of the SUV_max of the biopsied lesion, with the sum of 11 common oncogenic anomalies including TP53, EGFR, ALK, MYC, MET, FGF/FGFR, DNA repair, PI3K/Akt/mTOR (PAM), MEK, CYCLIN, and WNT determined by specific common genomic anomalies in tissue biopsies from breast, gastrointestinal and lung cancer patients. Our speculation was that higher number of total oncogenic anomalies cause metabolic reconfiguration,¹⁴ thus increased GMR and SUV_max.

SUV_max was positively correlated to the total number of oncogenic anomalies (r = 0.27, p = .005), in the biopsied lesion (Figure 1). The SUV_max was also positively correlated with TP53, as previously shown,¹³ and also with PAM anomalies (Table 1). Relationship between SUV_max and other 9 individual oncogenic abnormalities, individually, was not statistically significant (Table 1). Representative images from two patients with total number of oncogenic anomalies of 0 and 5 and respective SUV_max of 4.9 and 23.6 are shown (Figures 2 and 3).

Table 1. Mean SUV_max and number of patients with or without genomic alteration (n = 102)

Gene	Mean SUVmax, number of patients		p value
	With genomic alteration	Without genomic alteration
TP53	9.0 (n = 47)	6.3 (n = 55)	0.002*
PAM	8.7 (n = 40)	6.8 (n = 62)	0.042*
EGFR	7.6 (n = 28)	7.5 (n = 74)	0.98
MEK	8.9 (n = 18)	7.2 (n = 84)	0.16
CYCLIN	7.8 (n = 39)	7.4 (n = 63)	0.68
WNT	8.2 (n = 17)	7.4 (n = 85)	0.52
ALK	11.9 (n = 3)	7.4 (n = 99)	0.09
MYC	8.7 (n = 14)	7.4 (n = 88)	0.31
MET	6.0 (n = 2)	7.6 (n = 100)	0.63
FGF/FGFR	6.8 (n = 21)	7.7 (n = 81)	0.40

*Among 11 common oncogenic alterations, only TP53 and PAM alterations were significantly related to SUV_max.

Figure 2. FDG PET projection image in a patient with adenocarcinoma of the lung. The biopsied right lung hypermetabolic lesion had SUV_max of 20.2 (arrow). This lesion had a total number of main oncogenic alterations of 5

Figure 3. FDG PET projection image in a patient with adenocarcinoma of the lung. The biopsied right lung hypermetabolic lesion had SUV_max of 4.9 (arrow). This lesion had a total number of main oncogenic alterations of 0

Our finding suggests that higher SUV_max is an indicator of total number of oncogenic anomalies. We speculate that higher number of oncogenic anomalies cause metabolic reprogramming by stimulating glucose uptake, and channeling glucose to aerobic glycolysis,^14,17,18 therefore increase SUV_max. We suggest that SUV_max merits further study as an accessible and noninvasive surrogate for the total number of oncogenic anomalies derived from sequencing a tissue biopsy.

This study had several limitations including its retrospective nature, relatively small number of patients, and lack of a definite underlying mechanism. Also, the imaging and molecular testing were not exactly concurrent, because of the retrospective nature of the study.

Conclusions

¹⁸F-FDG PET/CT SUV_max is positively correlated with total number of oncogenic anomalies (r = 0.27, p = .005). This finding suggests that SUV_max can estimate the total number of oncogenic anomalies, noninvasively. Since mutational load has been implicated as a predictive factor for immunotherapy response,^1,9

Patients and methods

Patient selection

We studied 102 consecutive untreated patients including 42 breast, 26 gastrointestinal and 34 lung cancer patients who underwent 18F-FDG PET/CT, within six months before a biopsy for genomic profiling, a subpopulation of our prior work¹³. A 6-month cutoff was chosen to avoid a false positive or a false negative SUV_max secondary to post-biopsy inflammatory changes or long time-lapse, respectively. Although concurrent imaging and molecular testing is ideal, the retrospective nature of this study precluded achieving concurrent imaging and molecular testing. Genomic profiling included 11 common DNA anomalies: (1) TP53, (2) DNA repair, (3) EGFR, (4) PI3K/AKT/MTOR (PAM) pathway including PTEN, PIK3CA, AKT, TSC, CCNB1, MTOR, FBXW2, and NF2, (5) MEK, (6) CYCLIN including CCND,CDK, CDKN, and RB, (7) WNT, (8) ALK, (9) MYC, (10) MET, and (11) FGF/FGFR. This study was performed in accordance with the guidelines of the UCSD Internal Review Board (PREDICT [Profile Related Evidence Determining Individualized Cancer Therapy], protocol; NCT02478931).

¹⁸F-FDG PET-CT imaging

All patients were asked to fast for at least six hours prior to their scan. Blood glucose levels were measured immediately before the FDG injection and no patient had a blood glucose level >160 mg/dl. Patients were intravenously injected with 370–740 MBq FDG, within a 5–10 second interval. Following an uptake period of approximately 1 hour in a quiet room at rest, a multi-station 3-dimensional (3D) whole body PET acquisition with CT, for attenuation correction, was performed for approximately 60 min, using a GE Discovery VCT scanner (GE, Waukesha, WI). Whole-body CT covers a region ranging from the head to the mid-thigh. PET images were acquired, after the CT scan, at a rate of 2 minutes/bed position, in the 3D acquisition mode. CT images were then reconstructed onto a 512 × 512 matrix. PET images were reconstructed using a standard whole-body 3D iterative reconstruction: 2 iterations; 28 subsets onto a 128 × 128 matrix with attenuation correction, decay correction, and scatter correction. The photon energy window was 425–650 keV. Slice thickness was 3.27 mm and reconstruction diameter was 70 cm. Pixel size was 5.47 mm × 5.47 mm with spatial resolution of 5 mm. ¹⁸F-FDG PET/CT images were generated for review on a workstation.

Image analysis

All PET images were reviewed and further analyzed on the institution’s pictures archiving and communication system (PACS), (AGFA Impax 6.3, Mortsel Belgium) by a board certified academic nuclear medicine physician. The lesions that were later biopsied were selected for imaging analysis. Focal activities of the biopsied lesions were manually identified on PET images. SUVs of the biopsied lesions were obtained by manually placing a circular region of interest (ROI) at the site of the maximum FDG uptake in the PET images and the maximal activity (SUV_max) was recorded. SUV was calculated as decay-corrected activity of tissue volume (kBq/mL)/injected FDG activity per body mass (kBq/g).

Genomic analysis

Genomic analysis was performed on the biopsy samples, using a clinical next generation sequencing (NGS) based assay (182 to 315 genes) (FoundationOne™, Foundation Medicine Inc., Cambridge, MA), to interrogate for DNA alterations including detection of base substitutions, insertions, deletions, copy number alterations, and selected gene fusions in 11 classes of genomic pathways: TP53, EGFR, ALK, MYC, MET, FGF/FGFR, DNA repair, PI3K/Akt/mTOR (PAM), MEK, CYCLIN, and WNT pathway genes were analyzed.

Statistical analysis

Statistical analysis was done in R, version 3.5.2 and Microsoft Excel. Differences between SUV_max in oncogene positive versus negative group patients were analyzed by the paired t-test (two-tailed) and considered to be significant at a p value less than 0.05. The Pearson correlation coefficient (r) was used in the linear relationship between the SUV_max and total number of oncogenic anomalies.

Ethical approval and consent to participate

This study was performed in accordance with the guidelines of the UCSD Internal Review Board (PREDICT [Profile Related Evidence Determining Individualized Cancer Therapy], protocol; NCT02478931).

Disclosure of potential conflicts of interest

Razelle Kurzrock receives research funding from Genentech, Incyte, Merck Serono, Pfizer, Sequenom, Foundation Medicine, Grifols, Konica Minolta, Omniseq and Guardant, as well as consultant fees from Loxo, X Biotech, NeoMed, and Actuate Therapeutics, speaker fees from Roche, and an equity interest in IDby DNA and Curematch Inc.

Additional information

Funding

Amin Haghighat Jahromi is supported by [NIH T32-4T32EB005970] grant. This work was also supported in part by the Joan and Irwin Jacobs Fund philanthropic fund; and by National Cancer Institute at the National Institutes of Health grant [P30 CA023100].